/skill-score

Grade a skill the way the router and a real user will experience it — then say plainly whether it ships, and if not, exactly what to fix.

Two things make a skill good: it fires when it should (the description is the router) and it does the job safely and well when it fires (the body is a contract, not a how-to essay). This skill scores both, applies the hard safety gates that override the number, and returns a verdict.

Governing principle: hard on Core, gentle on the user's own creations. A Core / first-party skill that scores below the bar does not ship — that is a hard gate we hold ourselves to. A skill the user wrote for themselves is coached, never blocked: show the score, name the one change that would make it fire, offer to make it — but always create/keep their skill if they want it.

Arguments

$TARGET: Optional.

• A skill name or path (triage, .agents/skills/triage/) → score that one skill.
• --all → portfolio pass over every shipped skill: routing collisions, stale references, bad frontmatter, and per-skill grades in one table.
• Empty → score the skill most recently created or edited this session; if none, ask which.

$ORIGIN: Optional. core (first-party, hard gate) or user (coach-don't-block). If omitted, infer: a -custom suffix or a path under .claude/skills-custom/ ⇒ user; anything else shipped in the repo ⇒ core.

Step 0: Prefer the script

The scoring math (Tier-1/Tier-2 point tally, length checks, when-trigger detection, reference-path existence, collision proximity) is deterministic. Run the script, don't recompute by hand:

python3 .agents/skills/skill-score/scripts/score_skill.py <path-to-skill-dir-or-SKILL.md> [--all] [--origin core|user] [--json]

The script returns the mechanical sub-scores, every hard-gate check it can decide from files alone, and a provisional grade. Then you (the model) do the judgment-only parts the script flags as NEEDS_MODEL: is the description actually distinguishable from its nearest neighbor in meaning (not just string distance)? Does the body inspect its own output before claiming success? Are the anti-triggers pointing at the right neighbor? Merge the script's tally with your judgment calls into the final verdict.

If the script cannot run (no Python), fall back to scoring by hand against the rubric below — say so in the output.

The hard gates (any one fails ⇒ verdict is NO, whatever the number)

These come straight from the ratified synthesis. A skill cannot ship if:

1. Indistinguishable from a neighbor. Its description cannot be told apart from its nearest existing skill — the router would coin-flip between them. (Fix: sharper outcome + anti-trigger naming that neighbor.)
2. Destructive / external / publish action without authority. It can delete, overwrite, send, post, or publish outside the user's vault without an explicit confirmation gate in the body.
3. PII into a shared artifact. Secrets or personal content can flow into anything that leaves the machine (a published DexDiff profile, an uploaded page, an external message) without a redaction or confirmation step.
4. Claims success without inspecting output. It tells the user "done / created / fixed" without reading back the thing it just produced or the tool result that proves it.

For a user-origin skill, gates 2–4 still warn loudly and gate 1 becomes advice ("this won't fire on its own unless we distinguish it from X — want me to?") — but they do not refuse to create the user's own skill. For a core-origin skill, any gate failure blocks the ship.

Tier 1 — universal must-pass (~60 pts). Every skill, every shape.

Tier-1 floor: a core skill must clear ≥50/60 on Tier 1 regardless of Tier 2.

Tier 2 — situational, scored only if the shape applies

Classify the skill's shape first, then score only the matching block. Do not cargo-cult these onto a plain conversational workflow — an inapplicable criterion is scored N/A, not zero.

Tier-2 available points vary by shape; normalize the final grade to 100 (earned / applicable * 100). The script does this.

Grade bands

• ≥85 — SHIP. Clears the bar. (Core: may merge. User: great, ship it.)
• 70–84 — REVISE. Close. Return the specific criteria that lost points; usually 1–2 description fixes.
• <70 — NO. Not ready. For core: does not ship. For user: coach, offer to sharpen, still create if they insist.
• Any hard-gate failure — NO, printed above the number with the gate named.

Step 1: Load and classify

Read the target SKILL.md (+ its dir). Determine origin (core/user) and shape (workflow / setup / generative / multi-session / research / script-bearing — a skill can be more than one). Note which Tier-2 blocks apply.

Step 2: Run the checks

Run .agents/skills/skill-score/scripts/score_skill.py. It returns: Tier-1 sub-scores, applicable Tier-2 sub-scores, hard-gate results it can decide, referenced-path existence, nearest-neighbor by description proximity, and a provisional grade.

Step 3: Model judgment (the parts a script can't)

For each item the script marks NEEDS_MODEL, decide it and record one line of evidence:

• Distinguishable-in-meaning? Read the nearest-neighbor's description; would you pick the right one from a real user utterance? If not → gate 1.
• Inspects its own output? Find the place the skill declares done. Does it read back what it made? If not → gate 4.
• Anti-trigger points at the true collision? The named neighbor must be the one users would actually confuse it with.
• Degradation honest? Trace the missing-prereq path.

Step 4: Run the trigger evals

Load evals/trigger-cases.yaml for the skill (see shape below). For each case, decide whether this description would fire. Positives must fire; negatives/collisions must NOT (they should route to the named neighbor); the ambiguous case should ask; the missing-prereq case should degrade honestly; the failure-recovery case should not claim false success. Report pass/fail per case. A core skill that fails a positive or a collision case cannot score ≥85.

Step 5: Verdict

Print, in this order:

1. VERDICT: SHIP / REVISE / NO + the numeric grade.
2. Any hard-gate failures, each named, with the one change that clears it.
3. Lost points, grouped Tier-1 / Tier-2, each with the concrete fix (ideally the rewritten line).
4. Trigger-eval results (X/8 passed; list failures).
5. For user-origin: the coaching line — never a refusal. For core-origin: the ship decision.

Keep it short and actionable. The output is a fix list, not an essay.

--all portfolio mode

Score every shipped skill and emit one report:

• Grade table (skill | origin | shape | grade | verdict).
• Routing collisions: pairs whose descriptions are too close to disambiguate.
• Stale references: references//script paths named but absent.
• Bad frontmatter: missing name/description, malformed YAML.
• Portfolio summary: how many core skills are below the bar (the ship-blocking list).

This is the health check behind the description-rewrite / consolidation program — run it before and after the overhaul to measure the win.

Anti-patterns (for this skill itself)

• Do not pass a skill just because its YAML is valid — that is create-skill's floor, not the bar.
• Do not invent a Tier-2 penalty for a shape that doesn't apply.
• Do not block a user's own skill. Coach it.
• Do not claim a grade without running the checks (or saying you fell back to hand-scoring).